CT may scan a volume of a patient. Different objects may be adjacent to each other in the volume. The CT data from the scan represents both objects. To assist in diagnosis, planning, or implant design, the objects may be segmented. For example, segmentation of human bones from three-dimensional (3D) CT images is important to many clinical applications such as visualization enhancement, disease diagnosis, implant design, cutting guide design, and surgical planning. The locations or voxels associated with each object are determined.
Hand tracing of the objects, especially in three-dimensions, is time consuming. Automatic detection remains a very challenging task due to the variety and complexity of bone structures, non-uniform density of bone tissues, blurred and weak bone boundaries due to the partial volume effect, and pathological cases such as osteoporosis. In particular, when neighboring bones are too close to each other, the separation of these bones becomes very difficult as the inter-bone gap is extremely narrow or even disappears if the bones border on each other. Consequently, traditional segmentation methods often produce overlapping boundaries. FIG. 1 shows a femur (upper) and tibia (lower) with automated segmentation resulting in a region 40 of overlapping boundaries. The segmentation may not be accurate due to this overlap error.